A Tale of Four Models: Modeling Insights from GHG Regulation Workshop

On December 6, 2013, the Bipartisan Policy Center, together with the National Association of Regulatory Utility Commissioners (NARUC) hosted a workshop on greenhouse gas (GHG) regulation of existing power plants under the Clean Air Act. In addition to discussing policy design and CO2 reduction options, the workshop explored the use of economic modeling to inform decision making about Clean Air Act section 111(d) regulation.

Modeling as a Tool

Economic modeling can be a powerful tool to simulate interactions between complex systems and the projected market conditions, which can differ across models with varying levels of certainty. Models can expand the power of our brains to think through a complex problem and to imagine the many implications and potential outcomes. Of course, the outputs of a modeling exercise are highly dependent on the input assumptions and have their limitations. Not every important factor in the complex decision-making processes that will go into section 111(d) planning and implementation at states and power plants can be accurately represented in a model. Thus, the one thing we can be sure of is that no one model can tell us the “right” answer. Rather, models are tools that, when used wisely, can highlight directional trends, bookend the range of outcomes, uncover hidden insights, turn abstract concepts into something tangible and bring clarity about the implications of potential scenarios. Models are helpful when their limitations are understood, but they have the potential to be distracting or misleading if taken out of context or considered in isolation.

Panelists’ modeling

Speakers on the December 6 modeling panel shared insights from their own modeling exercises and gave us a peek at their analytical processes and policy objectives. Panelists plan to release written descriptions of their results and assumptions. Transparency of the underlying assumptions is critical to understand the limitations, compare different approaches and assess the value of what we glean from such exercises.

The panel discussion included four presentations which are based on analyses with four different modeling platforms using four different sets of assumptions and policy scenarios:

Kentucky’s Assistant Secretary for Climate Policy, John Lyons, discussed the modeling capability his state has developed with the assistance of both the University of Kentucky and the Pacific Northwest National Laboratory. Lyons shared that Kentucky’s goals for the analysis are to facilitate long-term energy planning, identify the least-cost pathway to supply electricity, and simulate macroeconomic impacts of power sector changes, including impacts on the manufacturing sector in his state. With 92 percent of Kentucky’s generation in 2012 coming from coal and an average age of 47 years for its coal-fired generators, Lyons underscored Kentucky’s interest in understanding the least-cost pathway to meeting the state’s energy needs into the future. Kentucky’s analysis, which is based on modeling of 56 scenarios, projects a steady decline in Kentucky’s coal-fired generation, with 78 percent coal in 2020 and most of the remaining 22 percent from natural gas.

Bruce Phillips of The NorthBridge Group discussed the impacts and tradeoffs of alternative policy designs for GHG regulation under section 111(d). He suggested guiding principles for 111(d) to:

achieve meaningful emission reductions,

at reasonable cost, price, and rate impacts,

on a sustainable legal basis,

with practical implementation pathways,

that respect industry challenges, and

provide long term policy compatibility.

Phillips presented a framework for comparing policy designs, including mass-based and rate-based performance standards. Based on his modeling and analysis, Phillips concluded that a mass-based policy design offers advantages for regulating GHGs from power plants.

Dan Lashof summarized the Natural Resources Defense Council (NRDC) proposal to establish a system-wide, flexible approach through state-specific, rate-based performance standards for existing units. Lashof presented results of an updated reference case – with inputs largely matching the 2013 Annual Energy Outlook – that results in lower electrical demand and, thus, lower CO2 emissions compared to NRDC results released a year ago. Lashof also discussed projections from scenarios that represent the NRDC proposal, that test the availability of demand side energy efficiency, and that vary the stringency of performance standards. All of the policy scenarios decreased coal generation, increased demand side energy efficiency and achieved significant CO2 reductions. Based on the analysis, NRDC estimates benefits based on the social cost of carbon would far outweigh compliance costs.

Paul Bailey of the American Coalition for Clean Coal Electricity (ACCCE) presented two modeling scenarios intended to bookend the impacts of NRDC’s proposed level of stringency. The first mirrors the NRDC proposal with trading and credits for energy efficiency and renewable energy but varies the underlying assumptions, including the cost of demand side energy efficiency. The second ACCCE scenario restricts trading of emission credits to within state borders and does not allow renewable energy or energy efficiency to count towards compliance. The analysis projects costs and electricity rate impacts that are significantly higher than NRDC projections, as well as net job losses compared to NRDC’s projection of net job gains.

Input assumptions

Following the presentations, speakers engaged in a dynamic panel discussion which compared various input assumptions employed in their modeling exercises and noted convergence on some, divergence on others and a recognition of significant uncertainty regarding some assumptions. Not surprisingly, the difference in assumptions contributed to variation in the outcomes.

For example, there was considerable divergence between the speakers’ analyses on the assumed cost and availability of demand side energy efficiency as an option to reduce greenhouse gas emissions from the power sector. Modeling exercises typically use forecasts of electricity demand that include a consistent level of energy efficiency based on existing programs and standards. For purposes of modeling 111(d), analyses differ on whether they include new and increased energy efficiency as one means to reduce emissions for compliance. Of this group of analyses, only NRDC and ACCCE modeled energy efficiency as a compliance option1 . NRDC scenarios assume energy efficiency is available at 4.2 – 5.8 cents/kWh2, while ACCCE includes one scenario with energy efficiency available at 11 cents/kWh and another that assumes no flexibility to use energy efficiency for compliance. The most notable difference in the modeling results is between the NRDC scenario that includes lower cost energy efficiency and the ACCCE scenario that excludes both energy efficiency and renewable energy as potential options to reach compliance under a section 111(d) program.

In addition, there are likely other modeling inputs where our current vision doesn’t fully capture future realities. For example, panelists noted that models typically do a poor job projecting technology innovation, which could be an important factor for the future of electricity generation.

Key takeaway

Good modeling evolves and improves as analysts learn more about the problems they are trying to solve. Often it is the entire modeling process, rather than the results of any one run, that provides the most valuable insights. This process leads to a better understanding of what to expect and how to plan for a changing future.

1 The analyses by Kentucky and The NorthBridge Group do not include new or expanded energy efficiency programs as a modeled compliance option, but The NorthBridge Group does incorporate demand reductions in response to projected price increases.

2 According to Dan Lashof, the NRDC analysis assumes a total resource cost in the range of 4.2-5.8 cents/kWh to offer new and increased energy efficiency as a compliance option. The table on slide 5 in his presentation shows the 2.3 – 3.5 cents/kWh energy efficiency program costs paid by utilities. According to Lashof, NRDC assumed that the energy efficiency program cost paid by utilities represents 55 percent of the total resource costs of acquiring energy efficiency, and thus, calculated a total resource cost of 4.2-5.8 cents/kWh. This total resource cost provides a more “apples to apples” comparison to the 11 cents/kWh energy efficiency cost cited by ACCCE.